Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval

Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI), are commonly used to estimate the LAI. However, these indices commonly saturate at moderate-to-dense canopies (e.g., NDVI saturates when LAI exceeds three). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in the red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR), and the green chlorophyll index ( CI green ) formula. These improved indices are termed NDVI red&RE (red and red-edge NDVI), MSR red&RE (red and red-edge MSR index), and CI red&RE (red and red-edge CI). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured. We investigated the predictive power of nine VIs for crop LAI estimation, including NDVI, MSR, and CI green ; the red-edge modified indices: NDVI_Red-edge , MSR_Red-edge , and CI_Red-edge (generally represented by VI_Red-edge ); and the newly improved indices: NDVI_red&RE , MSR_red&RE , and CI_red&RE (generally represented by VI red&RE ). The results show that VI_red&RE improves the coefficient of determination (R2) for LAI estimation by 10% in comparison to VI Red-edge . The newly improved indices prove to be the powerful alternatives for the LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-edge channels (such as Sentinel-2) when applied to precision agriculture.